U.S. patent application number 13/495635 was filed with the patent office on 2012-12-20 for systems and methods for predicting the value of personal property.
This patent application is currently assigned to Enservio, Inc.. Invention is credited to James Fini.
Application Number | 20120323609 13/495635 |
Document ID | / |
Family ID | 47354406 |
Filed Date | 2012-12-20 |
United States Patent
Application |
20120323609 |
Kind Code |
A1 |
Fini; James |
December 20, 2012 |
SYSTEMS AND METHODS FOR PREDICTING THE VALUE OF PERSONAL
PROPERTY
Abstract
Accurate, appropriate valuation of the contents of a residence
is facilitated based on characteristics of the household and the
residence. These factors are used to estimate the proper value of
the contents and may be based, at least in part and in various
embodiments, on information collected in the course of the
insurance underwriting process and from public and non-public
consumer spending data.
Inventors: |
Fini; James; (Jackson,
WY) |
Assignee: |
Enservio, Inc.
Needham
MA
|
Family ID: |
47354406 |
Appl. No.: |
13/495635 |
Filed: |
June 13, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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61497689 |
Jun 16, 2011 |
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Current U.S.
Class: |
705/4 |
Current CPC
Class: |
G06Q 10/10 20130101;
G06Q 40/00 20130101; G06Q 40/08 20130101 |
Class at
Publication: |
705/4 |
International
Class: |
G06Q 40/08 20120101
G06Q040/08 |
Claims
1. A workflow system for assembling a predicted inventory of
property present in a home or business, the system comprising: a
database for storing data for a plurality of consumers or
businesses, the data comprising, for each consumer or business, (i)
categories of personal or business property typically found in a
home or business, (ii) a geographic location of the consumer or
business, and (iii) demographic characteristics of the consumer or
business; a segregation module for accessing a bulk source of
consumer or business spending data for personal property and
segregating the spending data based on the categories data, the
demographic characteristics and the geographic locations; an
aggregate lifetime spending determination module for accessing data
from the segregation module and compiling an aggregate lifetime
spending amount for at least one category of property based on the
segregated spending data and an amount of time since an inception
date, the inception date corresponding (i) in the case of a
consumer, to when the consumer became an adult, and (ii) in the
case of a business, to when the business began operations; and a
depletion module for applying to the aggregate lifetime spending
amount at least one depletion factor indicative of an average
property retention duration for the at least one category of
property.
2. The system of claim 1 further comprising a database of
historical personal property claims across a plurality of insurance
carriers comprising, for a plurality of claims paid to policyholder
claimants for each insurance carrier, (i) at least one category of
claimed property, (ii) an adjusted replacement cost value (RCV) for
each category of claimed property, (iii) quantities and ages of
items of claimed property, (iv) depreciation applied by the carrier
to items of the claimed property, (v) brands and vendors for items
of the claimed property, and (vi) policy limits applied by the
carrier for each claimed category of property, the depletion module
accessing data from the database of historical property claims and
computing the depletion factor based at least in part thereon.
3. The system of claim 2 wherein the database of historical
property claims comprises records spanning a plurality of insurance
carriers and a plurality of policyholder geographies and
demographies.
4. A workflow system for assembling an insurance product, the
system comprising: a database for storing policyholder data for a
plurality of policyholders, the policyholder data comprising, for
each policyholder, (i) categories of personal property covered by
an insurance policy associated with the policyholder, (ii) a
geographic location of the policyholder, (iii) demographic
characteristics of the policyholder, and (iv) data indicative of
when the policyholder became an independent adult consumer; a
segregation module for accessing a bulk source of consumer spending
data for personal property and segregating the spending data for
each policyholder based on the categories data, the demographic
characteristics and the geographic location associated with the
policyholder; a coverage determination module for accessing data
from the segregation module and compiling an aggregate lifetime
spending amount for each policyholder for at least one category of
personal property based on the segregated spending data and an
amount of time since the policyholder became an adult consumer; and
a depletion module for adjusting the coverage amount by applying
thereto at least one depletion factor indicative of an average
property retention duration for the at least one category of
personal property.
5. The system of claim 4 further comprising a database of
historical personal property claims across a plurality of insurance
carriers comprising, for a plurality of claims paid to policyholder
claimants for each insurance carrier, (i) at least one category of
claimed personal property, (ii) an adjusted replacement cost value
(RCV) for each category of claimed personal property, (iii)
quantities and ages of items of claimed personal property, (iv)
depreciation applied by the carrier to items of the claimed
personal property, (v) brands and vendors for items of the claimed
personal property, and (vi) policy limits applied by the carrier
for each claimed category of personal property, the depletion
module accessing data from the database of historical personal
property claims and computing the depletion factor based at least
in part thereon.
6. The system of claim 5 wherein the database of historical
personal property claims comprises records spanning a plurality of
insurance carriers and a plurality of policyholder geographies and
demographies.
7. The system of claim 4 further comprising a depreciation module
for adjusting the coverage amount for at least one category of
personal property covered by an insurance policy associated with
the policyholder by applying thereto at least one depreciation
factor indicative of an average decrease in value of personal
property over time for the at least one category of personal
property.
8. The system of claim 4 wherein the policyholder data in the
database for storing policyholder data for a plurality of
policyholders further comprises, for each policyholder, data
indicative of policyholder demographic information.
9. The system of claim 4 wherein the coverage determination module
compiles the coverage amount in part by summing across the
categories of personal property spending data based on the amount
of time since the policyholder became an adult consumer.
10. The system of claim 4 wherein the depletion module applies a
separate depletion factor to each category of personal property,
the depletion factor for a category depending on at least one of
(i) the category, (ii) an amount of time since the policyholder
became an adult consumer, or (iii) demographic information about
the policyholder's household.
11. The system of claim 7 wherein the depreciation module applies a
separate depreciation factor to each category of personal property,
the depreciation factor for a category depending on at least one of
(i) the category, (ii) an amount of time since the policyholder
became an adult consumer, or (iii) demographic information about
the policyholder's household.
12. The system of claim 4 further comprising a policy-generation
module for generating an insurance policy based at least in part on
the adjusted coverage amount.
13. A method of assembling an insurance product based on stored
policyholder data for a plurality of policyholders, the
policyholder data comprising, for each policyholder, (i) categories
of personal property covered by an insurance policy associated with
the policyholder, (ii) a geographic location of the policyholder,
(iii) demographic characteristics of the policyholder, and (iv)
data indicative of when the policyholder became an independent
adult consumer, the method comprising the steps of: using a
computer to access a bulk source of consumer spending data for
personal property; computationally segregating the spending data
for each policyholder based on the categories data, the demographic
characteristics and the geographic location associated with the
policyholder; using the computer to compile a coverage amount for
each policyholder based on the segregated spending data and an
amount of time since the policyholder became an adult consumer; and
computationally adjusting the coverage amount by applying thereto
at least one depletion factor indicative of an average property
retention duration.
14. The method of claim 13 wherein the depletion factor is computed
at least in part based on data indicative of historical personal
property claims across a plurality of insurance carriers, the data
comprising, for a plurality of claims paid to policyholder
claimants for each insurance carrier, (i) at least one category of
claimed personal property, (ii) an adjusted replacement cost value
(RCV) for each category of claimed personal property, (iii)
quantities and ages of items of claimed personal property, (iv)
depreciation applied by the carrier to items of the claimed
personal property, (v) brands and vendors for items of the claimed
personal property, and (vi) policy limits applied by the carrier
for each claimed category of personal property.
15. The method of claim 13 wherein the stored policyholder data
further comprise at least one of income level, marital status, size
of household, ages of household members, and genders of household
members.
16. The method of claim 13 wherein the bulk source of consumer
spending data for personal property is the Consumer Expenditure
Survey.
17. The method of claim 13 further comprising computationally
adjusting the coverage amount by applying thereto at least one
depreciation factor indicative of an average decrease in value of
personal property over time.
18. The method of claim 13 further comprising computing the
policyholder's premium levels.
19. The method of claim 13 further comprising computing a risk
score for the policyholder.
20. The method of claim 19, wherein the risk score is calculated
based a plurality on geographic and demographic risk exposure
variables.
21. The method of claim 20 further comprising computing reserve
requirements for an insurance company.
22. The method of claim 13 further comprising computationally
generating an insurance policy based at least in part on the
adjusted coverage amount.
23. A method of assembling a predicted inventory of property
present in a home or business based on data for a plurality of
consumers or businesses, the data comprising, for each consumer or
business, (i) categories of personal or business property typically
found in a home or business, (ii) a geographic location of the
consumer or business, and (iii) demographic characteristics of the
consumer or business, the method comprising the steps of: using a
computer to access a bulk source of consumer or business spending
data; computationally segregating the spending data based on the
categories data, the demographic characteristics and the geographic
locations; using the computer to determine an aggregate lifetime
spending by accessing the segregated data and compiling an
aggregate lifetime spending amount for at least one category of
property based on the segregated spending data and an amount of
time since an inception date, the inception date corresponding to
(i) in the case of a consumer, when the consumer became an adult,
and (ii) in the case of a business, when the business began
operations; and computationally adjusting the aggregate lifetime
spending amount by applying thereto at least one depletion factor
indicative of an average property retention duration for the at
least one category of property.
24. The method of claim 23 wherein the depletion factor is computed
based at least in part based on data indicative of historical
property claims across a plurality of insurance carriers
comprising, for a plurality of claims paid to policyholder
claimants for each insurance carrier, (i) at least one category of
claimed property, (ii) an adjusted replacement cost value (RCV) for
each category of claimed property, (iii) quantities and ages of
items of claimed property, (iv) depreciation applied by the carrier
to items of the claimed property, (v) brands and vendors for items
of the claimed property, and (vi) policy limits applied by the
carrier for each claimed category of property.
25. The system of claim 24 wherein the historical property claims
span a plurality of insurance carriers and a plurality of
policyholder geographies and demographies.
Description
RELATED APPLICATION
[0001] This application claims priority to and the benefit of, and
incorporates herein by reference in its entirety, U.S. Provisional
Patent Application No. 61/497,689, which was filed on Jun. 16,
2011.
TECHNICAL FIELD
[0002] Embodiments of the invention relate generally to systems and
methods for property insurance underwriting and claims.
BACKGROUND OF THE INVENTION
[0003] Consumers often purchase insurance to cover losses to real
and personal property. In many cases, insurance related to a home
or business may cover the physical structure ("Coverage A") and
personal property ("Coverage C"). For example, a typical
homeowner's policy covers losses of items within the home, such as
furniture, clothing, electronics, appliances, artwork, jewelry,
kitchenware and dinnerware, and other items. Renter's and
condominium owner's insurance cover many of the same items,
excluding fixtures and the like.
[0004] Insurance risks in homeowner's policies are based on the
costs to replace the structure and contents of a residence. The
industry best practice for setting the policy amount of Coverage A
is to determine the replacement cost of the structure in a total
loss, and use that value as an estimate of the cost to rebuild or
repair the structure to be of like kind and quality to the
structure prior to a loss. This replacement cost is estimated using
variables including, but not limited to, geography, square footage
of structure, roof type, foundation type, floor quality, and other
factors well known to those in the industry.
[0005] In setting the value of the coverage for personal property
(in a home as opposed to a condominium or apartment), conventional
practice is to set Coverage C as a percentage of Coverage A. This
is somewhat arbitrary, however, since the variables that are used
to set the value of Coverage A generally have little or no
correlation with the replacement cost for Coverage C. As a result,
the personal property insured by Coverage C in homeowner's
insurance policies is frequently overvalued or undervalued (i.e.,
the policy holder is overinsured or underinsured). If a
policyholder's personal property is overinsured, the policyholder
is paying premiums for more coverage than is needed, and if a
policyholder's personal property is underinsured, the policyholder
will not have enough insurance to cover or replace the personal
property in the event of an insured loss. In general, best-practice
insurance underwriting requires insuring to the value ("ITV") as
closely as possible, so that the insured coverage matches the
actual value of the insured property as closely as possible. As
used herein, the term "policyholder" connotes both actual and
prospective owners of insurance policies, and "contents" refers to
personal property at an insured residence (whether subject to
Coverage C or other insurance coverage).
[0006] Unfortunately, at present, if ITV for contents is pursued at
all, it is typically based on a full inventory and appraisal of the
policyholder's personal property--an inconvenient and costly
undertaking. For apartments and condominiums, the practice in
setting coverage limits for personal property is even less precise.
The insured may accept the recommendation, based on generic
averages, of an insurance professional who may or may not have
visited the insured residence. Alternatively, the insured may be
called upon to make an estimate of contents value with little
guidance.
BRIEF SUMMARY OF THE INVENTION
[0007] In various embodiments, the present invention relates to a
system and techniques that facilitate accurate, appropriate
valuation of the contents of a residence based on characteristics
of the household and the residence. These factors are used to
estimate the proper value of the contents and may be based, at
least in part, on information collected in the course of the
insurance-underwriting process and from public and non-public
consumer spending data.
[0008] Implementations in accordance with the invention may take
various forms. For example, the system may be maintained by an
insurance company for its own internal use, or may be realized as a
server-based system accessible to insurers (or policyholders) on a
transactional basis. In another embodiment, the system and
associated techniques and products allow insurance companies to
calculate information related to their underwriting, including but
not limited to premium levels, reserve requirements, and risk
exposure.
[0009] The invention is not limited to insurance applications. For
example, many homeowners or businesses may wish to estimate their
property contents for tax or other reasons, and advertisers may use
estimates of personal property owned by prospects to more
effectively target ads or marketing campaigns.
[0010] Accordingly, in a first aspect, the invention pertains to a
workflow system for assembling a predicted inventory of property
present in a home or business. In various embodiments, the system
comprises a database for storing data for a plurality of consumers
or businesses; the data comprises, for each consumer or business,
(i) categories of personal or business property typically found in
a home or business, (ii) a geographic location of the consumer or
business, and (iii) demographic characteristics of the consumer or
business. The system also comprises a segregation module for
accessing a bulk source of consumer or business spending data for
personal property and segregating the spending data based on
categories data, demographic characteristics and geographic
locations. An aggregate lifetime spending determination module
accesses data from the segregation module and compiles an aggregate
lifetime spending amount for at least one category of property
based on the segregated spending data and an amount of time since
an inception date; the inception date corresponds to (i) in the
case of a consumer, when the consumer became an adult, and (ii) in
the case of a business, when the business began operations. A
depletion module applies, to the aggregate lifetime spending
amount, at least one depletion factor indicative of an average
property retention duration for each category of property.
[0011] In some embodiments, the system further comprises a database
of historical personal property claims across a plurality of
insurance carriers. For example, the database may include, for a
plurality of claims paid to policyholder claimants for each
insurance carrier, (i) at least one category of claimed property,
(ii) an adjusted replacement cost value (RCV) for each category of
claimed property, (iii) quantities and ages of items of claimed
property, (iv) depreciation applied by the carrier to items of the
claimed property, (v) brands and vendors for items of the claimed
property, and (vi) policy limits applied by the carrier for each
claimed category of property, the depletion module accessing data
from the database of historical property claims and computing the
depletion factor based at least in part thereon. The database of
historical property claims may have records spanning multiple
insurance carriers and multiple policyholder geographies and
demographies.
[0012] In another aspect, the invention relates to a workflow
system for assembling an insurance product. In various embodiments,
the system comprises a database for storing policyholder data for a
plurality of policyholders; the policyholder data comprises, for
each policyholder, (i) categories of personal property covered by
an insurance policy associated with the policyholder, (ii) a
geographic location of the policyholder, (iii) demographic
characteristics of the policyholder, and (iv) data indicative of
when the policyholder became an independent adult consumer. A
segregation module accesses a bulk source of consumer spending data
for personal property and segregates the spending data for each
policyholder based on the categories data, the demographic
characteristics and the geographic location associated with the
policyholder. A coverage determination module accesses data from
the segregation module and compiles an aggregate lifetime spending
amount for each policyholder for at least one category of personal
property based on the segregated spending data and an amount of
time since the policyholder became an adult consumer. A depletion
module adjusts the coverage amount by applying thereto at least one
depletion factor indicative of an average property retention
duration for each category of personal property.
[0013] In various embodiments, the system further comprises a
database of historical personal property claims across a plurality
of insurance carriers comprising, for a plurality of claims paid to
policyholder claimants for each insurance carrier, (i) at least one
category of claimed personal property, (ii) an adjusted replacement
cost value (RCV) for each category of claimed personal property,
(iii) quantities and ages of items of claimed personal property,
(iv) depreciation applied by the carrier to items of the claimed
personal property, (v) brands and vendors for items of the claimed
personal property, and (vi) policy limits applied by the carrier
for each claimed category of personal property. The depletion
module accesses data from the database of historical personal
property claims and computing the depletion factor based at least
in part thereon. The database of historical personal property
claims may comprise records spanning a plurality of insurance
carriers and a plurality of policyholder geographies and
demographies.
[0014] In some embodiments, the system comprises a depreciation
module for adjusting the coverage amount for at least one category
of personal property covered by an insurance policy associated with
the policyholder by applying thereto at least one depreciation
factor indicative of an average decrease in value of personal
property over time for the at least one category of personal
property. The policyholder data may further comprise, for each
policyholder, data indicative of policyholder demographic
information.
[0015] In various embodiments, the coverage determination module
compiles the coverage amount in part by summing across the
categories of personal property spending data based on the amount
of time since the policyholder became an adult consumer. The
depletion module may apply a separate depletion factor to each
category of personal property; for example, the depletion factor
for a category may depend on the category and/or the amount of time
since the policyholder became an adult consumer and/or demographic
information about the policyholder's household.
[0016] In some embodiments, the depreciation module applies a
separate depreciation factor to each category of personal property,
the depreciation factor for a category depending on at least one of
(i) the category, (ii) an amount of time since the policyholder
became an adult consumer, or (iii) demographic information about
the policyholder's household. The system may also include a
policy-generation module for generating an insurance policy based
at least in part on the adjusted coverage amount.
[0017] In still another aspect, the invention relates to a method
of assembling an insurance product based on stored policyholder
data for a plurality of policyholders. The policyholder data
generally comprises, for each policyholder, (i) categories of
personal property covered by an insurance policy associated with
the policyholder, (ii) a geographic location of the policyholder,
(iii) demographic characteristics of the policyholder, and (iv)
data indicative of when the policyholder became an independent
adult consumer. In various embodiments, the method comprises the
steps of using a computer to access a bulk source of consumer
spending data for personal property; computationally segregating
the spending data for each policyholder based on the categories
data, the demographic characteristics and the geographic location
associated with the policyholder; using the computer to compile a
coverage amount for each policyholder based on the segregated
spending data and an amount of time since the policyholder became
an adult consumer; and computationally adjusting the coverage
amount by applying thereto at least one depletion factor indicative
of an average property retention duration.
[0018] The depletion factor may be computed at least in part based
on data indicative of historical personal property claims across a
plurality of insurance carriers, the data comprising, for a
plurality of claims paid to policyholder claimants for each
insurance carrier, (i) at least one category of claimed personal
property, (ii) an adjusted replacement cost value (RCV) for each
category of claimed personal property, (iii) quantities and ages of
items of claimed personal property, (iv) depreciation applied by
the carrier to items of the claimed personal property, (v) brands
and vendors for items of the claimed personal property, and (vi)
policy limits applied by the carrier for each claimed category of
personal property. The policyholder data may further include income
level, marital status, size of household, ages of household
members, and/or genders of household members. In some embodiments,
the bulk source of consumer spending data for personal property is
the Consumer Expenditure Survey.
[0019] The average decrease in value of personal property over time
can be captured by by applying at least one depreciation factor to
adjust the coverage amount. For insurance purposes, the method can
include computing the policyholder's premium levels and/or
computing a risk score for the policyholder. For example, the risk
score may be calculated based a plurality on geographic and
demographic risk exposure variables. The method can also include
computationally generating an insurance policy based at least in
part on the adjusted coverage amount, and beyond that, computing
reserve requirements for an insurance company.
[0020] In still another aspect, the invention relates to a method
of assembling a predicted inventory of property present in a home
or business based on data for a plurality of consumers or
businesses, where the data includes or consists of, for each
consumer or business, (i) categories of personal or business
property typically found in a home or business, (ii) a geographic
location of the consumer or business, and (iii) demographic
characteristics of the consumer or business. In various
embodiments, the method comprises the steps of using a computer to
access a bulk source of consumer or business spending data;
computationally segregating the spending data based on the
categories data, the demographic characteristics and the geographic
locations; using the computer to determine an aggregate lifetime
spending by accessing the segregated data and compiling an
aggregate lifetime spending amount for at least one category of
property based on the segregated spending data and an amount of
time since an inception date, where the inception date corresponds
to (i) in the case of a consumer, when the consumer became an
adult, and (ii) in the case of a business, when the business began
operations; and computationally adjusting the aggregate lifetime
spending amount by applying thereto at least one depletion factor
indicative of an average property retention duration for the at
least one category of property.
[0021] In various embodiments, the depletion factor is computed
based at least in part on data indicative of historical property
claims across a plurality of insurance carriers comprising, for a
plurality of claims paid to policyholder claimants for each
insurance carrier, (i) at least one category of claimed property,
(ii) an adjusted replacement cost value (RCV) for each category of
claimed property, (iii) quantities and ages of items of claimed
property, (iv) depreciation applied by the carrier to items of the
claimed property, (v) brands and vendors for items of the claimed
property, and (vi) policy limits applied by the carrier for each
claimed category of property. The historical property claims may
span a plurality of insurance carriers and a plurality of
policyholder geographies and demographies.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The present invention is described in detail below with
reference to the attached drawing, wherein:
[0023] FIG. 1 is a flow chart illustrating a method of obtaining a
contents value in accordance with an embodiment of the invention;
and
[0024] FIGS. 2 and 3 are block diagrams illustrating representative
systems in accordance with embodiments of the invention.
DETAILED DESCRIPTION
[0025] FIG. 1 illustrates the operation of a representative
embodiment of the present invention. Although the embodiment
involves an insurance application, this is solely for purposes of
illustration, and it should be understood that the principles of
the invention may be applied outside the insurance context.
[0026] Policyholder data is stored in a database 102, e.g., in the
form of a database record associated with each policyholder. The
policyholder data may be collected during underwriting or otherwise
obtained, and may comprise, without limitation, information
including categories of personal property covered by the
policyholder's insurance policy, the policyholder's geographic
location, and data indicative of when the policyholder became an
independent adult consumer.
[0027] Claim data is stored in a database 103, e.g., in the form of
a database record associated with each policyholder. Claim data
includes characteristics and values associated with items of
personal property that were the subject of actual claims against
insurance policies. This data represents the value of personal
property by product category actually present in households based
on insurance claim data. As explained in greater detail below, this
data may be used to calculate a depletion factor.
[0028] A bulk source of consumer spending data is also illustrated
as stored in a database 104, but in fact the database is typically
associated with government, academic or other professional sources
specializing in this type of data and making it available, freely
or by subscription, over the Internet (where it may be accessed by
a computer in step 106). The bulk source of consumer spending data
104 accessed in step 106 may be public and/or non-public, and in
some embodiments of the present invention, the bulk source of
consumer spending data 104 may consist of or include the Consumer
Expenditure Survey ("CEX") conducted by the U.S. Bureau of Labor
Statistics.
[0029] In step 106 the computer accesses the bulk source of
consumer spending data, and in step 108 computationally segregates
the data to extract only those categories of expenditures that are
covered under the policyholder's homeowner's insurance, such as
clothing, food, electronics and jewelry (and excluding other
categories such as travel, movies, gasoline and cable TV). In this
way, the bulk spending data is processed to include as many
relevant categories of goods as possible, and to exclude as many
irrelevant categories as possible. Since the ultimate objective is
to provide an estimate, great precision is not necessary.
[0030] The consumer spending data may be further parsed based on
characteristics common to policyholders and relevant to the value
of contents, e.g., geographic location and demographic variables
(such as income levels, marital status, age, gender, and size of
household). If the bulk consumer spending data is or may be
segregated according to such variables--e.g., in tiers each
corresponding to a range, such as income levels), then the data may
be further tailored to each policyholder record in the database 102
to the extent the records contain values for these variables.
[0031] In these ways, the bulk consumer spending data is filtered
based on relevant characteristics of the policy and of the
individual policyholder. The computer then compiles an aggregate
lifetime spending amount 110 for each policyholder based on the
segregated spending data and the amount of time that has elapsed
since the policyholder became an adult consumer. Finally, the
computer may computationally adjust the coverage amount by applying
at least one depletion factor (step 112) to at least one category
of insured personal property indicative of an average property
retention duration. This depletion factor may be derived using
historical property claim data relevant to the geographic and
demographic variables.
[0032] In some embodiments, the coverage amount is further adjusted
(step 113) by applying thereto at least one filtering factor which
either increases or decreases a category of spending based
demographic variables. For example, if the household contains no
male children, all spending data related to male children is
eliminated. Similarly, if there are several male children present,
the factor will more heavily weight the spending data related to
male children.
[0033] The coverage amount may optionally be further adjusted (step
114) by applying thereto at least one depreciation factor
indicative of an average decrease in value of personal property
over time. Typically the depreciation factor is applied to the cost
of the insured personal property after the depletion factor,
yielding the ITV amount 116. Of course, if the depreciation and
depletion factors are static coefficients, or even if they vary
over time and are applied as time series, their order of
application should not matter. But in embodiments where the value
of one or both factors depends on the compiled coverage amount to
which it is applied, the order can be important. Furthermore,
depending on the nature of the policy, either or both factors may
be omitted. For example, depreciation may not be relevant in the
context of a full replacement-cost policy.
[0034] The policyholder's premium levels 118 may be computationally
calculated based on the ITV amount 116. Alternatively or in
addition, a risk score 120 for the policyholder may be
computationally calculated, and this score may be based on a
plurality of geographic and demographic risk-exposure variables
whose values are contained in databases 102 and 103. Furthermore,
claim reserve requirements 122 for an insurance company may be
computed, for a particular policy, based on the ITV replacement
cost value ("RCV") or the ITV actual cost value ("ACV" or actual
cash value) for a given policy multiplied by a percentage factor
representing an estimate by claim adjusters of the portion of the
total value of personal or business property that will be the
subject of a property claim.
[0035] FIG. 2 illustrates a representative system 200 for
implementing the techniques described above. The system is
typically implemented in a central computing device, described in
greater detail below, that has a central processor, memory, mass
storage, input/output facilities, a display, etc., all of which are
conventional and not shown in FIG. 2. A coverage determination
module 202 communicates with a segregation module 204, which
accesses a bulk source 206 of public and/or non-public consumer
spending data by means of a conventional communication module 216,
which is typically configured for communication over local and
wide-area networks; for example, source 206 may be accessed via the
Internet. The segregation module 204 segregates the spending data
for each policyholder based on the variables discussed above. The
coverage determination module 202 accesses data from the
segregation module 204 and compiles a coverage amount for each
policyholder based on the segregated spending data and data
specific to each policyholder.
[0036] Policyholder data is stored in a policyholder database 212,
which contains information collected during underwriting or
otherwise obtained regarding each policyholder. Additionally, the
coverage determination module 202 communicates with a depletion
module 208 and a depreciation module 210, which apply depletion and
depreciation factors, respectively, to coverage amounts computed by
the coverage determination module 202. The depreciation factors
applied by the depreciation module 210 are indicative of the
decrease in value of the insured personal property over time.
[0037] The modules 208, 210 may draw upon a depletion and
depreciation database 214 for depletion factors and/or depreciation
factors or variable data useful in the computation thereof. The
depletion and depreciation database 214 may, for example, contain
depletion factors to be applied to categories of personal property,
which may in turn depend on the category of personal property, the
amount of time since the policyholder became an adult consumer,
and/or demographic information about the policyholder's household.
The depletion and depreciation database 214 may also contain
depreciation factors to be applied to categories of personal
property, which may depend on the category of personal property,
the amount of time since the policyholder became an adult consumer,
and demographic information about the policyholder's household.
[0038] For instance, some categories of insured personal property,
such as children's clothing and toys, may be depleted from the
insured personal property as children age, as the policyholders
donate items to charity or pass them on to others. Other categories
of insured personal property, such as food, may be depleted
relatively quickly from the policyholder's ownership. Still other
categories of insured personal property, such as clothing, jewelry,
or furniture, may have much longer ownership timeframes. The
application of depletion factors to the aggregate lifetime spending
yields the RCV of the policyholder's personal property, and the
application of depreciation factors to the RCV yields the ACV of
the policyholder's personal property.
[0039] In addition, the depletion factor may be computed based also
on the contents of a claim database 215, which contains records
specifying historical personal property claims across a plurality
of insurance carriers. The records comprise data relating to claims
paid to policyholder claimants for each insurance carrier, and the
data may include or consist of (i) at least one category of claimed
personal property, (ii) an adjusted replacement cost value (RCV)
for each category of claimed personal property, (iii) quantities
and ages of items of claimed personal property, (iv) depreciation
applied by the carrier to items of the claimed personal property,
(v) brands and vendors for items of the claimed personal property,
and (vi) policy limits applied by the carrier for each claimed
category of personal property. This data is helpful to computation
of a depletion factor because it reflects actual RCV data compiled
in the course of claims payment. Accordingly, in some embodiments,
the depletion module 210 accesses this data and computes the
depletion factor based at least in part thereon. For statistical
accuracy, large numbers (e.g., more than 200) of insurance carriers
and larger numbers of actual claims paid (e.g., more than 10,000)
across statistically varied geographies are desirable.
[0040] In alternative embodiments, the depletion and/or
depreciation factors are computed more generically, e.g., based on
broad statistical modeling or publicly available data, which is
desirably, although not necessarily, differentiated among
policyholders to reflect differing demographic characteristics. The
objective, as explained above, is to model current property
holdings based on historical spending estimates.
[0041] In some embodiments, a policy-generation module 220
assembles an insurance policy for a policy applicant based on the
computed RCV of the applicant's personal property, the information
supplied by the applicant in his or her policy application, and the
criteria conventionally employed by the insurance carrier in
writing homeowners' policies. The policy may be furnished to the
applicant in paper and/or electronic form.
[0042] The various modules described above may be implemented by
computer-executable instructions, such as program modules, executed
by a conventional computer. Generally, program modules include
routines, programs, objects, components, data structures, etc. that
performs particular tasks or implement particular abstract data
types. Those skilled in the art will appreciate that the invention
may be practiced with various computer system configurations,
including hand-held wireless devices such as mobile phones or PDAs,
multiprocessor systems, microprocessor-based or programmable
consumer electronics, minicomputers, mainframe computers, and the
like. The invention may also be practiced in distributed computing
environments where tasks are performed by remote processing devices
that are linked through a communications network. In a distributed
computing environment, program modules may be located in both local
and remote computer-storage media including memory storage
devices.
[0043] The central computing device 200 may comprise or consist of
a general-purpose computing device in the form of a computer
including a processing unit, a system memory, and a system bus that
couples various system components including the system memory to
the processing unit. Computers typically include a variety of
computer-readable media that can form part of the system memory and
be read by the processing unit. By way of example, and not
limitation, computer readable media may comprise computer storage
media and communication media. The system memory may include
computer storage media in the form of volatile and/or nonvolatile
memory such as read only memory (ROM) and random access memory
(RAM). A basic input/output system (BIOS), containing the basic
routines that help to transfer information between elements, such
as during start-up, is typically stored in ROM. RAM typically
contains data and/or program modules that are immediately
accessible to and/or presently being operated on by processing
unit. The data or program modules may include an operating system,
application programs, other program modules, and program data. The
operating system may be or include a variety of operating systems
such as Microsoft WINDOWS operating system, the Unix operating
system, the Linux operating system, the Xenix operating system, the
IBM AIX operating system, the Hewlett Packard UX operating system,
the Novell NETWARE operating system, the Sun Microsystems SOLARIS
operating system, the OS/2 operating system, the BeOS operating
system, the MACINTOSH operating system, the APACHE operating
system, an OPENSTEP operating system or another operating system of
platform.
[0044] Any suitable programming language may be used to implement
without undue experimentation the data-gathering and analytical
functions described above. Illustratively, the programming language
used may include assembly language, Ada, APL, Basic, C, C++, C*,
COBOL, dBase, Forth, FORTRAN, Java, Modula-2, Pascal, Prolog,
Python, REXX, and/or JavaScript for example. Further, it is not
necessary that a single type of instruction or programming language
be utilized in conjunction with the operation of the system and
method of the invention. Rather, any number of different
programming languages may be utilized as is necessary or
desirable.
[0045] The computing environment may also include other
removable/nonremovable, volatile/nonvolatile computer storage
media. For example, a hard disk drive may read or write to
nonremovable, nonvolatile magnetic media. A magnetic disk drive may
read from or writes to a removable, nonvolatile magnetic disk, and
an optical disk drive may read from or write to a removable,
nonvolatile optical disk such as a CD-ROM or other optical media.
Other removable/nonremovable, volatile/nonvolatile computer storage
media that can be used in the exemplary operating environment
include, but are not limited to, magnetic tape cassettes, flash
memory cards, digital versatile disks, digital video tape, solid
state RAM, solid state ROM, and the like. The storage media are
typically connected to the system bus through a removable or
non-removable memory interface.
[0046] The processing unit that executes commands and instructions
may be a general purpose computer, but may utilize any of a wide
variety of other technologies including a special purpose computer,
a microcomputer, mini-computer, mainframe computer, programmed
micro-processor, micro-controller, peripheral integrated circuit
element, a CSIC (Customer Specific Integrated Circuit), ASIC
(Application Specific Integrated Circuit), a logic circuit, a
digital signal processor, a programmable logic device such as an
FPGA (Field Programmable Gate Array), PLD (Programmable Logic
Device), PLA (Programmable Logic Array), RFID processor, smart
chip, or any other device or arrangement of devices that is capable
of implementing the steps of the processes of the invention.
[0047] The network over which communication takes place may include
a wired or wireless local area network (LAN) and a wide area
network (WAN), wireless personal area network (PAN) and/or other
types of networks. When used in a LAN networking environment,
computers may be connected to the LAN through a network interface
or adapter. When used in a WAN networking environment, computers
typically include a modem or other communication mechanism. Modems
may be internal or external, and may be connected to the system bus
via the user-input interface, or other appropriate mechanism.
Computers may be connected over the Internet, an Intranet,
Extranet, Ethernet, or any other system that provides
communications. Some suitable communications protocols may include
TCP/IP, UDP, or OSI for example. For wireless communications,
communications protocols may include Bluetooth, Zigbee, IrDa or
other suitable protocol. Furthermore, components of the system may
communicate through a combination of wired or wireless paths.
[0048] While particular embodiments of the invention have been
illustrated and described in detail herein, it should be understood
that various changes and modifications might be made to the
invention without departing from the scope and intent of the
invention. For example, embodiments of the invention may be
deployed more generically as a workflow system 300 for assembling a
predicted inventory of all personal property present in a home or
business, as shown in FIG. 3. In this case, a bulk source of
consumer or business spending data is again used, and the coverage
determination module 202 is replaced with a module 225 for
determining aggregate lifetime spend, which performs functions
similar to that of module 202. In particular, the module 225
accesses data from the segregation module 204 and compiles an
aggregate lifetime spending amount for at least one category of
personal property based on the segregated spending data and an
amount of time since an inception date--i.e., when a homeowner
became an adult consumer or when a business began operations. The
depletion module 208 applies to the aggregate lifetime spending
amount at least one depletion factor indicative of an average
property retention duration for the at least one category of
personal property.
[0049] From the foregoing it will be seen that this invention is
one well adapted to attain all the ends and objects set forth
above, together with other advantages, which are obvious and
inherent to the system and method. It will be understood that
certain features and sub-combinations are of utility and may be
employed without reference to other features and sub-combinations.
This is contemplated and within the scope of the appended
claims.
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